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International Journal of Computer Vision

, Volume 45, Issue 1, pp 25–38 | Cite as

Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns

  • John Daugman
Article

Abstract

Algorithms first described in 1993 for recognizing persons by their iris patterns have now been tested in several public field trials, producing no false matches in several million comparison tests. The underlying recognition principle is the failure of a test of statistical independence on texture phase structure as encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 244 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm2 over the iris, enabling real-time decisions about personal identity with extremely high confidence. This paper reviews the current algorithms and presents the results of 2.3 million comparisons among eye images acquired in trials in Britain, the USA, and Japan, and it discusses aspects of the process still in need of improvement.

statistical independence wavelets texture iris recognition decision theory 

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References

  1. Adini, Y., Moses, Y., and Ullman, S. 1997. Face recognition: The problem of compensating for changes in illumination direction. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7):721-732.Google Scholar
  2. Belhumeur, P.N., Hespanha, J.P., and Kriegman, D.J. 1997. Eigenfaces vs. Fisherfaces: Recognition using class-specific linear projection. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7):711-720.Google Scholar
  3. Berggren, L. 1985. Iridology: A critical review. Acta Ophthalmologica, 63(1):1-8.Google Scholar
  4. Chedekel, M.R. 1995. Photophysics and photochemistry of melanin. Melanin: Its Role in Human Photoprotection.Valdenmar: Overland Park, pp. 11-23.Google Scholar
  5. Cover, T. and Thomas, J. 1991. Elements of Information Theory. Wiley: New York.Google Scholar
  6. Daugman, J. 1980. Two-dimensional spectral analysis of cortical receptive field profiles. Vision Research, 20(10):847-856.Google Scholar
  7. Daugman, J. 1985. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A, 2(7):1160-1169.Google Scholar
  8. Daugman, J. 1988. Complete discrete 2D Gabor transforms by neural networks for image analysis and compression. IEEE Trans. Acoustics, Speech, and Signal Processing, 36(7):1169-1179.Google Scholar
  9. Daugman, J. 1993. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Analysis and Machine Intelligence, 15(11):1148-1161.Google Scholar
  10. Daugman, J. and Dowing, C. 1995. Demodulation, predictive coding, and spatial vision. Journal of the Optical Society of America A, 12(4):641-660.Google Scholar
  11. Kronfeld, P. 1962. Gross anatomy and embryology of the eye. In The Eye, H. Davson (Ed.). Academic Press: London.Google Scholar
  12. Pentland, A. and Choudhury, T. 2000. Face recognition for smart environments. Computer, 33(2):50-55.Google Scholar
  13. Phillips, P.J., Martin, A., Wilson, C.L., and Przybocki, M. 2000a. An introduction to evaluating biometric systems. Computer, 33(2):56-63.Google Scholar
  14. Phillips, P.J., Moon, H., Rizvi, S.A., and Rauss, P.J. 2000b. The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence, 22(10):1090-1104.Google Scholar
  15. Simon, A., Worthen, D.M., and Mitas, J.A. 1979. An evaluation of iridology. Journal of the American Medical Association, 242:1385-1387.Google Scholar
  16. Viveros, R., Balasubramanian, K., and Balakrishnan, N. 1984. Binomial and negative binomial analogues under correlated Bernoulli trials. The American Statistician, 48(3):243-247.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • John Daugman
    • 1
  1. 1.The Computer LaboratoryUniversity of CambridgeCambridgeUK

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